{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import datasets\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 条形图\n",
    "x = np.arange(8)\n",
    "y = np.array([1, 5, 3, 6, 2, 4, 5, 6])\n",
    "\n",
    "df = pd.DataFrame({\"x-axis\": x, \"y-axis\": y})\n",
    "\n",
    "sns.barplot(\"x-axis\", \"y-axis\", palette=\"RdBu_r\", data=df)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/distributions.py:2103: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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n5UOw2eCpr2UdJaxRwSOEgfnzr8NLL/0VS5Yswd/+9jdYLBbWkcgFWE0pNhj55DyA4+AqP8U6SlijgkcIA4sXX48tW7Ziy5YtaG9vx3e+8x2sX78ex44dYx2NfM3b2ABJvBYSDfuxaBJ1DGRZ4+GmgjcmVPAIYcTn86G2thY1NTXweDxISkrCxo0b8dRTT7GORtDbQ1MMV3d9+Lx8eBrq4OseeQpBMjjqpUkIA3/5y5+wZ8/bGDcuC7fddhueffbZ/jkwFyxYQMMJGBNcTnhbjeALZrCO0o/Pmwrbe+/AdeY0lFfMYR0nLFHBI4QBi8WC3//+ORQWDhwYrlarRzUVGAkOT1MTIAii6LDSR5qWDk4TC9eZU1TwLhE1aRLCgM/nRU7O5AHP3XfffQBAEz6LgJg6rPThJBLwuVPgPlNOq6BfIrrCIySEnn76cbS2mnD06Ffo7LRALpcCADweD6qrqxmnI308jfXgYjSQaBNYRxmAnzIVziOH4KmvhXz8RNZxwg4VPEJCaPnyFaiurkJlZQWuueZaaDS9a5xJpVLMnDmTcTrSx9NQD1l65qCrUbAkz/lmeAIVvNGjgkdICOXl5SMvLx9XXjkHBoNB1MvxRCvB7YbX2Ax+Sj7rKBeRqNWQjZ8AV/kpxFxPq8uMFhU8QkLo179+EI8++gTuv/8+cBwHqXTgbfTi4mJGyUgfT0sT4PNBls52Ds2h8Ln5sJW8DV9XFyS0ov2oUMEjJIRuv/0OAMC6db+CRMIhLo7dLPxkcJ6GrzusZIinw8r5+Lzeguc6S8MTRot6aRISQnl5UwAAM2deDr3egNmzZ8NiseDQoUOYMmUK43QE+LrDikoNSUIS6yiDkqZl9K6eUH6SdZSwQwWPEAaefPIxvPLK31FVVYVNmzahsbERDz30EOtYBF93WMkQX4eVPhzHQZ6bD/dZGp4wWlTwCGHgzJlyrF//P9i7dy9uvvlmPP7442hsbGQdK+oJHg+8LU2iGnA+GD4vH4LdDk9dDesoYYUKHiEMCIIPEokEn3zyCQoLCwEAdrudcSribWkGvF7IMsRd8OQ5uYBEQqsnjBIVPEIYSE/PxP33r0VDQwNmz56N+++/H7m5uaxjRb1vZlgRd8GTqNSQjZ9IBW+UqJcmIQxs2PAbfPTRfsyfPxdyuRxXXHEFbrrpJsapiKehHpxSBUlSMusoI+Lz8mF79y14Oy2QxmtZxwkLdIVHCAMqlQrTp89EV1cXTp48iYKCAppaTAQ8jfWQpWeItsPK+fgpUwGA1sgbBbrCI4SBLVuewxtv7EBy8jdXEhzH4YMPPmCYKroJXi88zY1QzZ3HOopfpIZUSLQJcJ0+CeWcuazjhAUqeIQw8OGH7+PVV3cjL28C6yjka15jM+DxiL7DSh+O48DnTYXji0MQPG5wMjnrSKJHTZokIDiOg9WFEf/zCOJvKgoFvd4AnU7HOgY5j6exAQAgFXmHlfPxU6YCLhfc1VWso4QFusIjAeHyCPiqomvE/a7Mi4OGD0Egkbv88ivxpz89i2XLrodSqex/furUqQxTRTdPQx04hQLS5PD5IiLPzgFkMrjKT4KfnMc6juhRwSOEgT173gYA7N//zT07uofHlqeuFrKMLHCS8Gn44ngF5JNy4Dp9ErhxJes4okcFjxAGXnvtLUgkHC0PJBKC2wVPUwNU869jHWXU+ClT0bPrdXhbTZDq9KzjiFpIv8pYrVYsX74cDQ29beVlZWUoKirC4sWLsXnz5lBGIYQpm82G3//+d7jjjjtgsVjwyCOPoKenh3WsqOVpbOhdEihrPOsoo8bn9TaD0yD0kYWs4B09ehTf//73UVNTAwBwOBzYsGEDtmzZgnfffRcnTpzAgQMHQhWHEKaeeeYpxMRo0N7eDoVCAavVikceeYR1rKjVNyelPAwLnjQpGVK9obdZkwwrZAXv1VdfxW9+8xvo9b2X3MeOHcO4ceOQmZkJmUyGoqIilJSUhCoOIUxVVJzB3Xf/HDKZDCqVCk8//TROnz494uuKi4uxdOlSLFq0CNu2bbto+/vvv48VK1bgxhtvxD333IPOzs5gxI847roaSBISw3ZBVT5vKtzVlRCcTtZRRC1kBe+xxx7DFVdc0f/YZDIN6Jat1+thNBpDFYcQpiQS6YDHXq8XkhE6SxiNRmzevBnbt2/H7t27sWPHDlRWVvZvt1qt2LhxI1588UW89dZbyM3NxfPPPx+U/JHGU1sDWdY41jEumXzKVMDrgaviDOsoosas04ogCBc9N9rpfMR6w1+niw35e/o6nNDEKkfcTyaXDrvf+dtG2nc0x+2jVvPQJSj8OuZosfjcL9VVV83B//3fC3A4HPjoo4/wyiuvYM6c4VevLisrQ2FhIbRaLQBgyZIlKCkpwb333gsAcLvd2LhxIwwGAwAgNzcXxcXFQf05IoGvqxM+Swfk376GdZRLJh8/EZxCCVf5SSguK2AdR7SYFTyDwYC2trb+xyaTqb+501/t7Vb4fBcXTpZ0uli0tnaH/H1tLsDa7RhxP4+bH3I/TaxywLbh9h3NcQfktPFo9bj8OuZosPrcL9Wdd96NbdteRmxsLJ555hlcffXVuOeee4Z9zWCtIseOHet/nJCQgIULFwLovUf+4osv4gc/+EFwfoAI4v76/p0sK3xnveFkMsgn58FdfgqCIITFXKAsMCt406dPx7lz51BbW4uMjAy8/fbbWLmSxpGQyHfgwIf417/+iaqqCqhUKuTm5mLWrFlQKIa/8vW3VaS7uxv33HMP8vLycPPNN48qm1hbTYDgXcGb2poBqRQp0/Mg4QM/K0KoWh7ksy9H8/GvEOewQJmVFZBjhlOriT+YFTyFQoEnnngC9913H5xOJ+bPn4/rr7+eVRxCQqKk5B28/PJf8eMf34XJk3ORkBCD48eP47HHHoPT6cTixYuHfK3BYMDhw4f7Hw/WKmIymfDjH/8YhYWF2LBhw6jzibHVBAjuFXxX+VnI0tLR3ukEENhOH6FsefClTQQAGMsOQa1KGPPxwq3VBMCIY1tDXvD27dvX/+errroKb731VqgjEMLM66/vwDPPvICUlJT+k3PSpEmYPn06NmzYMGzBmzt3Lp5//nmYzWaoVCqUlpbi0Ucf7d/u9Xpx991344YbbhixeZT0ErxeuOtrobyykHWUMZPExUGWkQnX6ZNQX7eEdRxRoplWCAkht9uNlJSUi56fMGECnCN0KTcYDFi3bh1Wr14Nt9uNW2+9FQUFBVizZg3Wrl2LlpYWnDp1Cl6vF++99x4A4LLLLsNjjz0WlJ8lEnibGwGXC/Jx4Xv/7nzyvKmwf/AefD09kMTEsI4jOlTwCAkhqXTooQeD3aO7UFFREYqKigY8t3XrVgDAtGnTUF5ePraAUcZ9rneVAdmESYyTBAY/ZSrs75fAdfY0lDOvGPkFUYYKHgmp3mWE/LtHpJRzkHHiu59EIoe7uhKSxGRItWO/5yUGsowscDEauE+fpII3CCp4JKT8XUYIiMylhKqqKrF48fz+x32dLAVBgMsV+OEaZGiCIMBdXQU+/zLWUQKGk0jA5+XDdfoEBJ8vrFZ+CAUqeISE0I4du/r/zHEcEhLU7MJEOa+pBYKtB/KJ2ayjBBSflw/nkUPw1NVAPn4i6ziiQgWPkBBKSUnt/zMtD8RW3yrh8gi5f9dHnjsFkEjgOn2SCt4F6HqXEBKV3NUVkMTFQZKUzDpKQElUasjGT6TVEwZBBY8QEnUEnw/uirOQZ+dG5DRcfF4+vM2N8HZaWEcRFSp4hJCo421uhNBjhTwnl3WUoOCn9C4K6y6nq7zzUcEjhEQd19neZXTkOXmMkwSH1JAKiTaBmjUvQJ1WRMwjcHC4/RuH5hMir1mGkGBxV5RDakiFND6edZSg4DgOfN5UOL44BMHjBieTs44kClTwRMzhFvB5uX9j1mbkhOdKzYSEmuB2wX2uCsqrrmYdJaj4KVPh+PTj3rGGkyPzSna0qEmTEBJV3NVVgMcDPkKbM/vIsycDMjk1a56HCh4hJKq4Th4H5Dzk2TmsowQVx/f+jC7quNKPCh4hJGoIggDXqePgJ+eBk0fYvHWD4PPy4WtrhbfVxDqKKFDBI4REDW9jPXydFvBTp7GOEhJ9wxPoKq8XFTxCSNRwnjgGcFxETRg9HGliMqT6FLqP9zUqeISQqOE6eQyyCZMgiYmeOUz5vHy4qyshjLDAcDSggkcIiQqe5iZ4W5qhmDaDdZSQkk+ZCni9cFXQ4sBU8AghUcF55BAgkUAxYxbrKCElnzAJnEJJzZqggkcIiQKCzwfnl4fB5+VDoollHSekOKkU8sl5cJefgiD4N3NTpKKCRwiJeO7Ks/B1dUIxazbrKEzwU6bC19UJb1Mj6yhMUcEjhEQ8x2dl4FSqqOmdeSE+Nx8ADU+ggkcIiWhecztcx7+Ccs63wMmjcxJlSVwcZBmZcJ06wToKU1TwCCERzf7JAYDjoPzWPNZRmOKnFsBTVwOvpYN1FGao4AWAR+BgdQFWF2DscPb/ebD/PLSMDyEh47Pb4Tx0EIqCmZBqE1jHYUoxvbd3quvYl4yTsEPLAwXA+cv4aGKVsHY7htz3yrw4aCJ/Cj9CRMH+4V4IDgdU11zHOgpzUp0e0vQMOI9+CdW8a1nHYYKu8AghEcnbYYb9o/1QzLoSsvRM1nFEQTF9Vm+zprmNdRQmqOARQiKS7d23AAhQX7+cdRTRUEyfCQBwHo3OZk3mTZqrV69Ge3s7ZLLeKJs2bcL06dMZpyKEhDPn0S/g/OoIVItugDQhkXUc0ZAmJkOWOQ7Oo19AvWAR6zghx7TgCYKA6upq7N+/v7/gEUKGVlxcjBdeeAFutxt33nknbr/99kH3e+CBBzBnzhzccsstIU7InrfDDOsb/4YsazzU1y1hHUd0FNNnoeftN+FtNUGq07OOE1JMmzSrq6vBcRzWrFmDG2+8Ea+88grLOISImtFoxObNm7F9+3bs3r0bO3bsQGVl5UX73H333SgpKWGUki1fjxVdf90C+ATEfn81OKmUdSTR4WfMAjgOjiOHWEcJOaaXVV1dXbjqqquwceNGOBwOrF69GhMmTMC3vvUtv16flCSOJT58HU5oYpX9j8//84XUah66BMUlHXc4MrnUr31H2u/8bf4eM5Dvf77RfFYAoNNF9hyJZWVlKCwshFarBQAsWbIEJSUluPfee/v3KS4uxnXXXde/TzTxWbvR9bc/w2tuR/xP7oE0Wcc6kihJ47WQ506B8/BnUC9eCk4SPV05mBa8mTNnYubM3puoarUat956Kw4cOOB3wWtvt8LnYz8Zqs2F/qEIIw1LsNl4tHpcoz7uSDxu3q99h9vvwuz+HjNQ73+h0XxWOl0sWlu7/dpXLCQSblRf2kwmE3S6b36J6/V6HDt2bMA+P/nJTwAAR44cuaRMYvkSOZjhvtA46uvRsOU5eC0WZNx7L2JnzAhdsBGI8YuY4roFaPzTn6BuqYFmmD4TYsw+FkwL3uHDh+F2u3HVVVcB6L2nR/fyCBncYDPdc1xgJzIQy5fICw31hUbw+WD/z4ewvfc2OJUacXevhSN9PBwi+fIj1i9iQkY2uBgNjHv3wZ42cdB9xJp9OCN9iWR6Ldvd3Y0nn3wSTqcTVqsVb775JhYtir6eQ4T4w2AwoK3tm/FTJpMJen10dTo4n7e9DZ1/fg62d3aBz52ChHUPQJ41nnWssMDJZFBePhuuU8ejaqoxpgVvwYIFmD9/Pm666SasXLkSK1eu7G/iJIQMNHfuXBw8eBBmsxl2ux2lpaWYNy8654d0fHkYls1PwNvcCM33/guxd6yBJDaOdaywovzWPEAQ4Cj7iHWUkGHefvjf//3f+O///m/WMQgRPYPBgHXr1mH16tVwu9249dZbUVBQgDVr1mDt2rWYNm0a64hBJ7hdsO56Hc5DByEbPxGxt91B4+wukTQxCfxl0+H49BOoFy4Bx/vfQSxcMS94hBD/FRUVoaioaMBzW7duvWi/J554IlSRQsZnt6Pr5RfhOVcF1XVLoF50Aw07GCPVtxfAdfwrOD7/DKooWE0ievqjEkLCltdmQ+dfnoen9hxib7sDMdcvp2IXALLxEyAbN6F3km23m3WcoKOCRwgRNcHjRsNzz8Hb3Ii4O9ZAMeNy1pEiBsdxUF+/HL5OCxyffsI6TtBRwSOEiJYgCLC+/m/Yysuh+d5/gZ8ylXWkiMNnT4Y8ezJs+0rhc/g3RjZcUcEjhIiW89BBOI8cQvKKFVDOupJ1nIilvqEIQo8VttJ3WUcJKip4hBBR8rQ0w7rrNchz8pC8YgXrOBFNnjUeyjlz4fh4PzyN9azjBA0VPEKI6AheL6w7/glOoUTsbaujar5HVtQ33AguRoPuV7dHbAcW+ldECBEd+/734Wmoh+aW70Kiiaz5HMVKolZD853vw9vUgJ633mAdJyio4BFCRMXb1grb+yXgC2ZCUUAzL4WSIn8aVNcshOPTT9Cxfz/rOAFHA89DjOM4WF3+Tc7rEwI7MXC4Gc1nhU4XrP4trAClnIOME98EyaRXT/FOcFIpYm6MvsVrxUB9/XJ4WprQ8ve/I/b7AhQzr2AdKWCo4IWYyyPgq4ouv/adkRPdcwOO5rO6Ij8Bh8v92/fKvDho+LEkI8HiOn0CrlMnoF62AtJ4Les4UYmTShG3+sew//1FdP/7n/DZ7VDN/TbrWAFBTZqEEFEQ3G5Yd78Bqd4A1dXXsI4T1Tg5j8x16yDPnYKeN1+FdeerENx+NqGIGBU8Qogo2P+zD772NsSsuBUcrYvJnESpRNwda6CctwCOgx/B8uxT8DQ1sI41JlTwCCHMeTvMsH3wHvhpM8BPzmMdh3yNk0qhKboFcT+5Bz6bDZbnnoZtXykEr5d1tEtCBY8QwlxP8ZsAgJiimxknIYPhc6cg4f7/AT91Gmx7itG55Rl4TEbWsUaNCh4hhCnX2XK4jn8F9XVLaG07EZPEaBD7Xz9C7O13wttmgmXz72D/zz4IPh/raH6jhnJCCDOCx42eXa9BkpQM1fxrWcchI+A4DooZl0M+MRvWN/6NnuI34Tp1ArG33xkWK87TFR4hhBn7hx/A22qC5ubvgpPJWcchfpLExSP2zp9C893b4a6rgeXZp+Cur2Mda0RU8AghTHjbWmHb9x746bPA505hHYeMEsdxUF5ZCO3P/z9AIkHnlmfgPPYl61jDooJHCAk5QRBgffNVcDIZYopoRpVwJkvPgHbtLyHLyET3Ky/BXvYR60hDooJHCAk55+efwn22HOrrl0MaH886DhkjiSYW8T/9OfgpU9Hz5qvoKX0XgiC+6fuo4BFCQsprbkPPW29APikHyqsiY8oq0js7S+zqn0BxxRzY9+5Bz85XRdeDk3ppEkJCRvB40L39HwDHQfO9/6J17iIMJ5VC893bIdHEwr7/ffhsVsR+f7VoOiRRwSOEhExP8U54as8h9r9+SGPuIhTHcYhZtgISTSx63n4TXT09iL1zDSRKFeto1KRJCAkN+8GP4Sj7CKr510ExfRbrOCTIVPOvhWbVarjPVaHzhefg6/JvNZNgooJHCAk651dH0PPmq5BPmQr1DUWs45AQUV5+JeJ++FN4W02wPP803HU1TPNQwSOEBJXj00/Qvf3vkE2YhLgf/AicVMo6EgkhPm8qtPf8AuA4dG55Bvayj5j14KSCRwgJCsHtgnXX67C+8W/Ic6cg7od3gZP7t/JuT+m7Ax7XPP54/3MX/v/C1w23/cL9+vaxvPDsRdvaH3tkwDH6nmt/7JH+59oe/O/+17c9vB49pe/C8sKzsLzwLNofewQV99/fv73vdW0Pr+8/Tt/+ff8///37nu973YV5z/8ZhtpnuM9rpM/RHxe+x1BkGVnQ/uJXkGdPRs+br6Lrr1vgNbf59R6BxLzgFRcXY+nSpVi0aBG2bdvGOg4hojbS+XL69GmsXLkSS5YswUMPPQSPxxPyjIIgwHX6BCzPPAnHJwegvPoaxN35U0iUSr+PYd+7Z+DjM2f6n7vw/xe+brjtF+7Xt4+nuvKibYKlY8Ax+p4TLB3fHOjrZXI81ZWA0wH73j3wVFfCU10JwdIBT3t7//b+1zkd/cfp27/v/+e/f9/zfa+7MO/5P8NQ+wz3eY30OfrjwvcYjiQmBnE/uhsxN90Kd001Op58DNa3dsLb2enXewUC016aRqMRmzdvxs6dO8HzPFatWoU5c+YgOzubZSxCRMmf82X9+vX47W9/ixkzZmDDhg149dVXcdttt4Ukn6+rE86Tx+E8VAZPQz0kicmIW/NzWt+O9OMkEqi+NR/81ALYSt+F4+P9cHxyAHz+NPCXTQc/JR8SdUzQ3p9pwSsrK0NhYSG0Wi0AYMmSJSgpKcG9997LMhYhojTS+dLY2AiHw4EZM2YAAG655RY899xzASt4vh4rfD1WCA4HBLsdgq0HXnM7vK0meOpq4G01AQCkKanQrFwFxZWFdL+ODEqqTUDsd2+H+trFsB/8CM4vj8B14ijAcZDqUyBLS4dUb4AkLh6S2DhwmlhwCh6cQjWmmXmYFjyTyQSdTtf/WK/X49ixY36/XiLhghFr1GRSQK3sPbFVvAQ+5dAnuUzK9e878nEDv+9w+12YPdTvH8p9xTDeebT/fkc6Xy7crtPpYDSObpHOoTJ5GuvR/Y+/DbqN02jAT5wE2TULwU+YAIk+BRw3tnNTnpw8IIs8Obk/X9+2C/cZar+hjt/nwn3P39a3fbDXnJ/zwtdc+PoLc/mz72iPMdg+w31e/n6OwxnuGP6Q6PWQr1gJoehmeFua4K6qgLelGR5jC9z1NYO+Rl10CxSXFQx+vBEycALDCc/+/Oc/w263Y926dQCA1157DcePH8emTZtYRSJEtEY6X7744gs89dRT+Ne//gUAqK2txV133YWSkhJmmQkRE6bfcw0GA9ravumpYzKZoNfrGSYiRLxGOl8u3N7a2krnEyHnYVrw5s6di4MHD8JsNsNut6O0tBTz5s1jGYkQ0RrpfElPT4dCocCRI0cAALt27aLziZDzMG3SBHq7Wf/lL3+B2+3GrbfeijVr1rCMQ4ioDXa+rFmzBmvXrsW0adNQXl6Ohx9+GD09PcjPz8fjjz8Onvdv7BshkY55wSOEEEJCQQR91QghhJDgo4JHCCEkKlDBI4QQEhWo4BFCCIkKVPAIIYREBSp4hBBCogIVPEIIIVGBCh4hhJCoQAWPEEJIVKCCRwghJCpQwSOEEBIVqOARQgiJClTwCCGERAUqeIQQQqICFTxCCCFRgQoeIYSQqEAFjxBCSFSggkcIISQqUMEjhBASFajgEUIIiQpU8AghhEQFKniEEEKiAhU8QgghUYEKHiGEkKhABY8QQkhUoIJHCCEkKlDBI4QQEhWo4BESRoqLi7F06VIsWrQI27Ztu2j7H//4RyxYsAArVqzAihUrBt2HkGglYx2AEOIfo9GIzZs3Y+fOneB5HqtWrcKcOXOQnZ3dv8+JEyfwhz/8ATNnzmSYlBBxois8QsJEWVkZCgsLodVqoVarsWTJEpSUlAzY58SJE9i6dSuKioqwadMmOJ1ORmkJEZ+wvsLr6OiBzyewjjFAUpIG7e1W1jEuCWUPLYmEQ0JCjN/7m0wm6HS6/sd6vR7Hjh3rf9zT04MpU6bggQceQHp6Oh588EFs2bIF69atC2huQsJVWBc8n08QXcEDIMpM/qLs4iUIF/98HMf1/zkmJgZbt27tf/yjH/0IGzZsGFXBa2+3ivJz1Oli0drazTrGqIVrbiA8s0skHJKSNENvD2EWQsgYGAwGtLW19T82mUzQ6/X9j5uamvD666/3PxYEATJZWH+nJSSgqOAREibmzp2LgwcPwmw2w263o7S0FPPmzevfrlQq8dRTT6G+vh6CIGDbtm1YtGgRw8SEiAsVPELChMFgwLp167B69WrcdNNNWL58OQoKCrBmzRocP34ciYmJ2LRpE372s5/h+uuvhyAI+OEPf8g6NiGiwQmD3RgIE2K83xCO7d59KHtojXS/gQUxnlNAeP79AuGbGwjP7HQPjxBCCAEVPPI1p8+BI+bPccp2FG7BzToOIYQEHHXhIjC6mrGtdSuSlUlwuF0o7SjGasPPkChLYh2NENHjfIDX5R10W1e7DT7HN9ukvBQCXWYwQwUvytm8PdjW+iIK1Ffg8tSZsFhsOGM7gX8Yt+Du1F9CKVGxjkiIqHldXtScNA26TROrhLXb0f94/FQ9JEppqKKRC9B3jShX0rEb6fw4TFLl9j+Xq74MBj4NJR272AUjhJAAo4IXxYyuJlQ6TmNmzOyLts2KuQpn7afQ5GpgkIwQQgKPCl4UO9BZinxVAeQS/qJtvITHNPVMfGjZwyAZIYQEHhW8KNXl6USV4yxyVFOH3CdbNQWNrjqY3C0hTEYIIcFBBS9KfWH9FBOU2eAHubrrI+PkyFFOwefdn4QwGSGEBAcVvCgkCAKO9hzGRGXuiPvmqKbgeM8XcPlcIUhGCCHBE9SCV1xcjKVLl2LRokXYtm3bkPvt378f1157bTCjkPM0uerhhRfJMv2I+8ZIY6GTG1BuPx6CZIQQEjxBK3hGoxGbN2/G9u3bsXv3buzYsQOVlZUX7dfW1obf/e53wYpBBnHSdhTjFBMHrKU2nPHKHHxpPRTkVIQQElxBK3hlZWUoLCyEVquFWq3GkiVLUFJSctF+Dz/8MO69995gxSAXEAQBp+3HkKWY4PdrshTj0eiqQ483vFYUJ4SQ8wVtphWTyQSdTtf/WK/X49ixYwP2+cc//oH8/HxMnz79kt5DbDPN99HpYllHGJLR0QJPsxuTkscPeoWn1aoHfd0Ex0Q0SCtwtW7eoNvFQMyfOyGEvaAVvMFWHTr/F+zZs2dRWlqKl19+GS0tl9btXYxLmYh9SY3Pug4jVZ6Jzk77Rdu0WjUsFtugr0vhsvCp8VPkYmawI14SsX/ugxHj8kCERLKgNWkaDAa0tbX1PzaZTNDrv+kkUVJSgtbWVqxcuRI//elPYTKZcNtttwUrDvnaWfsppMkzRv26dD4Lja5aOHwXF0pCCAkHQSt4c+fOxcGDB2E2m2G321FaWop5875pDlu7di3ee+897N69Gy+++CL0ej22b98erDgEgFtwo8FVi1R+9AVPLpHDIE9Hhb08CMkIIST4gnqFt27dOqxevRo33XQTli9fjoKCAqxZswbHj1MXdxYanDVIlCWBlygu6fXpikycsZ8IcCpCIo8gCDA3daP6aAvaG7sgfH3rRcJx8Dm8fv3H+Rj/EBEoqMsDFRUVoaioaMBzW7duvWi/jIwM7Nu3L5hRCIBqRwUM8rRLfn06Pw7vdLwOn+CDhKM5CwgZjMftReWhJvRYHAAHQABM1RZkX5kGj8eHuvJWv45DSwkFHv3WiiLVjgoY+EsveBppLNSSGDS4agOYipDIIfgEVB1ugq3LifHTDZj3ncswcVYKHD0uVHzeBI978IViSWhQwYsSbp8LJncTdPKUMR0njc9CJd3HI2RQxnMdsJodGDdNj6SMOHASDgmpsZg4MxX2LidOfVTHOmJUo4IXJRpddUiQJUPOycd0nDQ+AxX20wFKRUjkcDs8aK4wI94Qg6SMuAHb4g0x0I2LR9WXzbB1ORklJFTwokSNoxr6MV7dAYBenoo2jwk2b08AUhESOYzVHfD5BGTkJQ+6PS03CTJeiuYKc4iTkT5U8KJEnbMaOrlhzMeRclKkyNNxzlkRgFSERAaP24vWuk4kpsZCqRl8yS2ZXIpJM1NhabHC3k1XeSxQwYsCPsGHRlfdmO/f9THwaXQfj5DzdDR1w+cVoJ+gHXa/SbNSIZFyaKnsCE0wMgAVvCjQ5jFBIVFCJRl8nszRSuMzUOU4G5BjkdGhJbfEqb2hG8pYHur44ce4KtRyJGfFw9zcDbfTE6J0pA8VvCjQ4KwJSHNmH600ER7BjQ5Pe8COSUZGS26JU1e7DT0WB5LS4/xacis5Mx4QAHNjeM39Ggmo4EWBemcNkvxY7NVfHMchlc9AtYPu44USLbklTnUnTACApHT/VutQxfKISVCira5z0En2SfBQwYsCja46JMsDV/AAwCBPQ5X9TECPSYY32JJbRqNxwD5jXXKLjI4gCKgvb0OcTg250v+Jq5Iz4+DocaOnwxHEdORCQZ1ajLDn8jlh9rQjUTZ4V+lLlcqn46ueQxAEwe+V08nYhGLJLTEvVyTG9Q7bm7pg63Qi54p0aGKVg+5z/vNymRSaWCVUOXLUn2xFV6sNKeMSBn2dSs0jLikw990vlRg/87Ggghfhml2NSJAlQcoFdk4+jTQOMk6GVo8xIOP7yMgMBgMOHz7c/3i4Jbfcbnf/klujWYVEjGtMAuJd7/D0p/UAAGU8D2v3xVdrmljlgOcTPd7+x/H6GLTWdSI1JxGc5OIvjXabC04fu6nIxPqZD2ekNSapSTPCNbnqkSTTjbzjJTDI03GO7uOFDC25JT5NZ9uRkKIBP4rmzD4JqRp4XF50m2mNyVChghfhGpy1AW/O7GPgU1FNwxNChpbcEhdHjwvmxm6kThq8SXIk8foYSKQcOprD6yoqnFGTZoRrctdjkio3KMdOkafjsLWMlgsKIVpySzxavp4iLDU7EZY226hfL5FKEK+PgaWlB1lThUGbNUlg0W+pCObw2WH1diFeemnfQEcSI9VAwSlhcjcH5fiEiJnxnAWKGDni9TGXfIy+Zk0r9dYMCSp4EazF1YhEWXJQr75S+DScc1w8+JmQSCYIAkznLNCP146pl3JcshocB3SaaDL2UKCCF8Gavy54wWSQp9F9PBJ1utvtcFhdI86dORKpXApNkgqdRmtggpFhUcGLYH1r4AWTQZ6GOuc5+ARfUN+HEDExnbMAAPTjtWM+llavgaPHDUePa8zHIsOjghfBml0NQb/CU0tjoJKo0eJuCur7ECImphoL1PEKxCQMPth8NOINvfcAqVkz+KjgRSiXz4VObwe0ssSgv1cKn4Yauo9HooQgCGitsUA3xvt3fRRqOZQaHp1GKnjBRgUvQpnczdBKEwM+w8pg6D4eiSZdrTa47B7ox8UH7Jjx+hh0m+3wutnNrBINqOBFqFB0WOlj4NNQT/fxSJRor+8CACRlBq7gaQ0xgAB0to5+PB/xHxW8CNXsqg9JcyYAqCRqqKUaNLsaQ/J+hLDU1tAFXi2HJnHs9+/6xGiVkMoldB8vyKjgRajeK7zgzKE5GIM8jebVJFGhvb4LyRn+LfbqL07CIU6nRlerjdbICyIqeBHIJ/jQ5jEiQZYUsvdMkaeh2kn38Uhkc/a4YDXbkZQZF/Bjx+ti4HF5Yet0BvzYpBcVvAjU7mmFShIDXsKH7D0NfBoanLXwCnTTnUSu9obeiZ6TMgJf8OJ0vWvfdbZSs2awUMGLQC2uppB1WOmjlKgQK41Dk6s+pO9LSCi11XeBk3BITAv8QrlyhQxqrQJdJuq4EixU8CJQs6sBWllwJoweDt3HI5GuvaETCakaSOXBGe4Tr4tBj8UBj4taSoIhqAWvuLgYS5cuxaJFi7Bt27aLtu/duxdFRUVYtmwZHnzwQbhcNLVOILS4GoM+pdhgUvh0VFPBIxHK5/XB3GQNSnNmn76VF6hZMziCVvCMRiM2b96M7du3Y/fu3dixYwcqK7+ZjcNms2HTpk146aWX8M4778DpdOLNN98MVpyoYnSHvkkTAAzyVDS66uAW3CF/b0KCraPZCp/Hh+QgdFjpo45XQMZLqVkzSIJW8MrKylBYWAitVgu1Wo0lS5agpKSkf7tarca+ffuQnJwMm82G9vZ2xMUF7x9StLB6u+ERPIiRBP4ew0h4iQIJskQ0OGtD/t6EBFt7w9cDzoN4hcdxvcMTOlt7IPhoeEKgBW3Fc5PJBJ3um3Fger0ex44dG7CPXC7HgQMH8Ktf/Qp6vR5XX331qN4jKSn0v9T9odPFMnvv9q566FV6JCRc2qKUWq16TO+f5R6HFkkNZutmjuk4l4Ll504in7mxG6o4Hqo4RVDfJ14XA3NjNzparEieqA3qe0WboBW8wQZPDjZQc/78+fjss8/whz/8ARs3bsTvf/97v9+jvd0Kn8i+Bel0sWht7Wb2/me6qqARtLBYRt8kotWqL+l150sQ9DhhPoqr+IVjOs5osf7cL4VEwon2Sxu5WEdzNxLTgv+lqm94Qkt1BxW8AAtak6bBYEBbW1v/Y5PJBL1e3//YYrHg448/7n9cVFSEM2fOBCtO1Gh2NSAhRFOKDUYvT4XJ3QKnz8EsAyGB5rK7YTU7kJAa/IIn46WI0SrRUt0R9PeKNkEreHPnzsXBgwdhNptht9tRWlqKefPm9W8XBAHr169HU1PvOmp79uzBrFmzghUnahjdTUx6aPaRcTLo5AbUOKuYZSAkEDgf4HN44XN40V7be/9Oq4vpf67vv2CI18ego8UKh5V6rgdS0Jo0DQYD1q1bh9WrV8PtduPWW29FQUEB1qxZg7Vr12LatGl49NFHcdddd4HjOGRnZ+N///d/gxUnKngFL8yetpBNGj2UFHkaqu1nkauayjQHIWPhdXlRc9IEAGiuNAMAui122E8OLEJZeYGfszZOr0bT2Xa0VHVg/HRDwI8frYJW8IDeZsqioqIBz23durX/zwsXLsTChaG91xPJ2txGaCRxkHFB/WsdUQqfgUPdHzHNQEgg2SwOKNRyyII04PxC6jgFFDFytFSaqeAFEM20EkFaGI2/u1CSTIdubxe6vV2soxASED2dTqi1we2deT6O45AyIQEtVR2i65gXzqjgRZAWVyOTKcUuJOEkX8+6QqsnkPDndnrgdngQEx+49e/8kTIxAW6HB+YG+uIYKFTwIkiLqymkSwINJ0Wejio79boNNJquL/Rslt7letQhLnj68VpwHNDy9f1DMnZU8CJIbw9NcRS8ND4D1Y6ztJhlANF0fWz0dPYOsVHHh65JEwB4pQxJmXForqThCYFCBS9CWL3d8ApeqBlMKTaYWGk8OI5Dq8fIOkrEoOn62OixOKDU8JDKQv/rMiU7EZYWK+zdtChsIPj1N3jfffehrKws2FnIGPStgTfYbDYscByHVD6DmjWH8NBD6/H555+N6jWDTddnNA78QtE3Xd+CBQvQ0dEx6un6yECCIMDW6URMCDusnC81u3eIUUsVXeUFgl/91xcvXowtW7bgf//3f/Hd734XK1euhFarDXI0MhpGdxPz8XcXSpVnoNJRjqvi5rOOIjrz51+Hl176KzZvftLvcyoU0/WJeaqzUM+V2tVug1wqhcflRYIhFprYwe/hyWXSIbcBGLBtpH3Pp1Lz0GfEoyxegY66LsxelDO6HyAAIm1+Wr8KXt94uqqqKrzxxhv4zne+gxkzZuAHP/gBCgoKgp2R+KF3SjFx3L/rk8pn4GD3fngED/OxgWKzePH1uP76G2CxGP0+pwwGAw4fPtz/eLDp+k6cONF/VVdUVIR169aNKpcY56cF2MyV6nN40drY20NSppDC2j34dHmJHu+Q2zSxygHbhtv3QnabC06fF/qJCag91QpjSyck0tA1q0bi/LR+f3o+nw+1tbWoqamBx+NBUlISNm7ciKeeeiogQcnY9DZpiqvgKSRKaGWJqHeeYx1FlEZ7TtF0faFn63QAHKCK45llSMlOhMfp7V+eiFw6v752b968GTt37kRmZiZuu+02PPvss5DL5bDZbFiwYAHWr18f7JxkGB7Bgw5Pu+iaNAEgRZ6BCns5JihD3xwjZn/5y5+wZ8/bGDcuy+9ziqbrC70eiwPqWEVIr6wuZJioBSfh0FzRAd04LbMckcCvgmc2m7F161bk5eUNeF6tVo/q/gAJjla3EXGyeEhF2GyYxmfgy55DAIpG3DeaWCwW/P73z6GwcOAV2EjnFE3XFzp9HVYS0tje15QrZEjOjENLpRkFCycwzRLu/Pra4vV6Lyp29913HwBQLzARMIqwObOPTm5Ah6cdPV4r6yii4vN5kZMzecBzdE6Ji7XDAa/HF/IZVgaTkp2ITlMPbF00PGEshr0k+M1vfgOj0YgjR47AbP5mtL/H40F1dXXQwxH/NLsaES8VX3MmAEg4KVL5dFQ5zqAg5nLWcZh7+unH0dpqwtGjX6Gz0wL515MR0zklPpaW3i9pai37gpeak4jjH5xDS6UZE2elso4TtoYteLfeeisqKipw5swZLFmypP95qVSKmTNnBj0c8U+zuwGTlfmsYwyp7z4eFTxg+fIVqK6uQmVlBa655lpoNL2/TOmcEh9zixWchINKw67DSp84nRqqOAUVvDEatuBNmzYN06ZNw7e+9S0YDLREhRgJggCjqwmFsfNG3pmRND4TpZbdEARBNAPjWcnLy0deXj6uvHIODAaDqMe9RbuOFivUcQpwEvb/ZjmOQ2p2AupOtMLn9THtRBPOhi14v/jFL/Dss8/iJz/5yaDbi4uLgxKK+K/Ta4GUk0IlUbOOMqQ4WTxknAxGdxNS+HTWcZj69a8fxKOPPoH7778PHMdBesEvLjqnxEHwCbAYrUhMF8/A65ScRFR/0YK2uk7oJ7BfFSUcDVvw1qxZAwD49a9/HZIwZPRaXI2iWANvJGl8Jirt5VFf8G6//Q4AwLp1v4JEwiEuTsU4ERlMV5sNXrc4Oqz0MUxIgETKoemsmQreJRr2uviyyy4DAMyePRupqamYPXs2LBYLDh06hClTpoQkIBlei6tRdDOsDCaVz0SF4zTrGMzl5fWeNzNnXg693kDnlEiZm3pnGIkRQYeVPjJeCv14LZrOttMqJJfIr4bgRx55BFu3bkVVVRU2bdqExsZGPPTQQ8HORvzQ5KoPi4KXIk9Hk6sBLh91qwaAJ598DK+88nc6p0Sqo6kbMl4KRYycdZQB0nKT0NPhQHebjXWUsORXwTtx4gQ2btyIvXv34uabb8bjjz+OxsbGYGcjfmhxNyFRpht5R8bkEjmS5XrUOKtYRxGFM2fKsX79/9A5JVLmxm4kpGhE18kqdXLvl9vGM+2Mk4QnvwqeIAiQSCT45JNPUFhYCACw2+1BDUZGZvfZYffZECeNZx3FL6nydFTYqVkTAATBR+eUSHk9PliMPUhMFV8PWnWcAgmpGjRRwbskfhW8rKwsrFmzBg0NDZg9ezbuv/9+5ObmBjsbGUGLqwFJIloDbyRpfBYqHeWsY4hCenom7r9/LZ1TImRpsULwCUhIFU8PzfOl5SbB3NgNh9XFOkrY8Wvyxccffxx79+7F5ZdfDrlcjiuuuAI33XRTkKORkTS7GpEQBj00+yTKkuHw2dHhMSNBhBNdh9KGDb/BRx/tx/z5c+mcEpm+DisJqRqY6joZp7lY2uQknNxfi6az7TQIfZT8usJTq9W44oor0NXVhZMnT6KgoICmQRKBRmddWAxJ6MNxHNL4TFTZ6SpPpVJh+vSZdE6JkLmxG0oNL4oZVgYTb4iBOl6BprPUrDlafl3hPfXUU3jllVeQlPRNb0CO4/DBBx8ELRgZWbO7ARNVk0feUURS+QyctZ/GFbFzWUdhasuW5/DGGzuQnPzNFxY6p8Sho6kbiemxor1VwHEc0nKTUP1FCzxuL2Rfz8dKRuZXwduzZw9KS0tpejERcfoc6PJ2QisNrwGoaXwmPu/+BF7BCykXvSfqhx++j1df3Y28PFruRUxcDg+62+0YVyDu33Vpk5NQeagJxuoOpOeGTysPa341aaamplKxE5m+Fc4lYVY0VBI1NNI4NLrqWEdhSq83QKcT/3CSaNPx9f27xDRxdljpoxsXD7lCSr01R8mvK7yrrroKTz75JK677joold/MPDB16tSgBSPDa3LVh8X4u8Gk8RmosJ9GliJ6r24uv/xK/OlPz2LZsuvpnBKR/g4rjBd9BQAJx8Hn8A653TAhAc1nzfDaPJApZRBoPukR+VXwdu7cCQAoKSnpf47uN7DV6KoT7aKvI0nlM3Dc9gWuw1LWUZjZs+dtAMD+/d+cQ3ROsWdu7IYmUQVeJR+22ISCx+NDXXnrkNvlShmcNjdOflKHy749DhJleLX2sOBXwdu3b98lHby4uBgvvPAC3G437rzzTtx+++0Dtr///vt4/vnnIQgCMjIy8PjjjyM+PjwGUbPW5KrHhNgc1jEuiV6eija3CTZvD9TSGNZxmHjttbcgkXC0PJDImJu6oR+vZR3DL3F6NcABFlMP6yhhw6+L4J6eHmzatAl33HEHLBYLHnnkEfT0DP8hG41GbN68Gdu3b8fu3buxY8cOVFZW9m+3Wq3YuHEjXnzxRbz11lvIzc3F888/P7afJko4fHZ0ezuhDdOxbFJOihR5Os45K1hHYcZms+H3v//dqM4pElz2Licc3S7R37/rI5NLEZuoQufXK7OTkflV8H77298iNjYW7e3tUCgUsFqteOSRR4Z9TVlZGQoLC6HVaqFWq7FkyZIBTaJutxsbN27s7wyTm5uL5ubmMfwo0aPJ1YBEmQ4SLnwb7VP4NFRE8Xi8Z555CjExmlGdUyS4zGHSYeV8WoMGjh43us00LZ0//PqNefr0aaxbtw4ymQwqlQpPP/00Tp8efk5Ek8k0oBeaXq+H0Wjsf5yQkICFCxcCABwOB1588cX+x2R4Ta46JIVph5U+6XwWqhxnonaZk4qKM7j77p+P6pwiwWVu7AYn4aBNCZ9m9nhDb9bmSjPjJOHBr3t4EsnAuuj1ei967kKD/SIbbCBnd3c37rnnHuTl5eHmm2/2J04/sd7/0OmC+w3R2NmAzPgMaOMCv8q5VhualdPjBRW4LgFCnA16ZUpAjhnszz2QeF4+4N+vP+cUCa72hi5oDTGQhtFAboVaDlUcj+ZKM/LmZbGOI3p+Fbwrr7wSTz31FBwOBz766CO88sormDNnzrCvMRgMOHz4cP9jk8kEvV4/YB+TyYQf//jHKCwsxIYNG0Ydvr3dCp9PXFcIOl0sWlu7g/oe57qrkS29DBZLYNfE0mrVAT/mcAyyDHze9AXmxH57zMcKxeceSJddNh2bNv2/UZ1TJHh8Xh/Mjd2YMCswX75CSavXoLnKDKfNDYVaXOv3iY1fXyl/+ctfQq1WIzY2Fs888wzy8vLwq1/9atjXzJ07FwcPHoTZbIbdbkdpaSnmzZvXv93r9eLuu+/GDTfcgIceeki00/iITZfHAg88iJXGsY4yZql89C4X9LOfrYVarRrVOUWCx9LSA6/Hh+TM8OslHm+IAQSguYIGoY9kxCu8vXv34m9/+xvOnDkDpVKJ3NxczJo1CwqFYtjXGQwGrFu3DqtXr4bb7catt96KgoICrFmzBmvXrkVLSwtOnToFr9eL9957DwBw2WWX4bHHHgvMTxahGlx10MlSIuILQiqfgU+7/wOP4IGM86uxISIcOPAh/vWvf6KqqgIqlcrvc4oET1t976oIyZnh90VSHa+AUsOj6YwZ46eH3xVqKA37W2bXrl3YsmUL1q5di7y8PHAch+PHj+Oxxx6D0+nE4sWLhz14UVERioqKBjy3detWAMC0adNQXh69vfQuVb3zHJLl4d1hpY9SokK8VIsGZy3GKyexjhMSJSXv4OWX/4of//guTJ6ci4SEmFGdUzS2NTja67ugjldAFRd+Xzo4jkNqdiLqTprg9fggldG94KEMW/D++c9/4uWXX0ZaWlr/c5MmTcL06dOxYcOGEU9OEnh1znOYqp7BOkbApPDpqLSXR03Be/31HXjmmReQkpLSP/Dc33Oqb2zrzp07wfM8Vq1ahTlz5iA7OxvAN2Nb33jjDRgMBjz77LN4/vnn8fDDD4fqxwtLgiCgrb4L+vHh+8UgLTsR575qgemcBak54Tk+NxSG/SrgdrsHFLs+EyZMgNPpDFooMjiP4IHR3YxkWeRM5J3GZ6IqilZBd7vdSEm5uNnJn3OKxrYGh83igMPqQlIY3r/ro8uKh4yX0hp5Ixj2Ck8qHbp7brSOn2Kp2dUArTQBcknk9MTSyVPQ5mmNmmnGpNKhv2OOdE4NNrb12LFj/Y8HG9v6gx/8YFT5xDrUBwjesBNzTe/9u5yCVCSf9x5d7TZoYpVDvayfXCYddr/zt42072iOO+A94pTIytfBWGlGcpIGnCQw9/jDaaiPP6Knp0AEqHVUQSePrJvSfdOMVTsqcFnMDNZxRC0UY1vFONQHCO6wk3MnTZAppPDJMeA9fA4vrN2OEV+f6Bl6P02scsC24fYdzXEvZLe5kDw+HtVfteDssWYkpo+9UIXbUB8AI85PO2zBO3PmDGbNmnXR84IgwOVyjT0dGZUaZzXS+UzWMQIuhU9DpaM8KgpeVVUlFi+e3/+4r175c06FYmxrNGqv70JSelzAropYkHAc9Jnx4Dig8VQrtEmDTyAh5aVRvYzQsAVv7969ocpBRuATfGhw1mBmzGzWUQIujc/Evs49EAQhIoZbDGfHjl39f+Y4DgkJ/s9sM3fuXDz//PMwm81QqVQoLS3Fo48+2r/9/LGt99xzTyBjRyyX3Y1OUw8ypoT3quEejw/N1R2ISVCh9oQJMYmqQfcbP1Uf1csIDVvw0tPTQ5WDjMDkboFCoozI+1zx0gQIgg/tnlYky/UjvyCMpaSk9v95tMsD0djWwGut671/pwuTJYFGojXEoOF0G826MgS6hxcmapyVMPAX95iNBBzHIZXPRJXjTMQXvLGisa2BZTpngVQmCcg9LzGI/7rgdRp7oJ+gZR1HdKK4NTe8VNsrYJCnjrxjmIrmacYIO6YaC5Kz4iJmsLYyhodSw8NipDXyBhMZf8sRzif4UOusQoo8cpuYU/lM1Dmr4RE8rKOQKOHocaHLZIuY5sw+WkMMus12eNxe1lFEhwpeGGh2NUAt1UTk/bs+SokS8bJE1DtrWEchUaL16/F3+ggreH2TSXeZQrfySbigghcGqhxnkRrBV3d9UuUZqIziVdBJ8HG+3vF1PocXxkozZLwU8Ynq/ufO/y9cxWiVkPFSatYcBHVaCQMV9lOYrMpnHSPo0vhMfNHzKRZhOesoJEJ5XV7UnDRBEAQ0nm1HTIISdadbB903Ky88J2nnOA7xhhh0NPdOIiAJ4/GFgUZXeCLn8NnR4m5CCh/5V3g6uQGdng5YveE1uwMJPw6rGy67B/G6yLxNoDXEwOfxwdpOzZrno4InctWOszDIUyHjIn9MjYSTIJXPQJXjDOsoJMJ1tfYAAOL1/g/8DydxyWpwEg4WYw/rKKJCBU/kym0nkMZnsY4RMql8Bs7aT7GOQSJcp6kHSg0PXhWZXyQlUgnidGp0mnpoov/zUMETMZ/gQ4XjNDIV41lHCZl0PgtVjjPwCT7WUUiE8np8sHY4Ivbqro/WEAOX3QN7F8173IcKnojVOauhlmigkUbGLBD+iJFqoJbEoNFVxzoKiVBdbTYIPgFxEXr/rk+8vvfns5iot2YfKngidsL2FbKi6OquTxqfSc2aJGgszVZI5RLEDjHBcqSQK2SISVCis4Xu4/WhgidSPsGH07ZjGKfIZh0l5NL5LCp4JCi8Hh8sph5oUwK3SKqYaQ0xsHU54bK7WUcRBSp4InXOWQmVRI14mZZ1lJDTy1Ng8ZjR5elkHYVEGGONBT6PDwkp4l3ZPZC0ht6fk3pr9qKCJ1JfWQ9hgjKHdQwmJJwUaXwmKhw0mTQJrMYzbZDKJIhNjuwOK32UGh7KGDksLXQfD6CCJ0oOnx1n7aeituABQLoiC+W246xjkAjicXvRVGGGNiUmqmYf0aZq0G22w+2kidmp4InQsZ4vkMZnQiWJjm+hg0nnx6HWWQW3j7pUk8BoPN0Gj8uLpIw41lFCKiElFhCATmrWpIInNoIg4LPu/yBHNYV1FKaUEiWSZHpUOypYRyER4tyXLYjRKqGJ8N6ZF1LF8eDVcnRQsyYVPLGpdPSuFhDJa9/5K0MxDqftx1jHIBHAarajtbYT46fpwXHR05wJ9E4mnZCiQVebDS5HdDdrUsETEUEQcKCzFPnq6VF3Ug4mUzEBZ+wnadaVKHT+Mj4+hxdd7bZBl/DxObzg/PjnUX2kGeCAcZfpgx9ehBJSNYAANFeaWUdhipYHEpEqxxlYvd0YH4Vj7wYTK41DjESDWmc1JijpM4kmfcv49NHEKmHtdgy67/ipekiU0iGP5XJ4UHWkGZn5OqhiFQHPGg7U8QrIlTI0nmnDhCtSWcdhhq7wRMIrePGeZTdmaGZDwtFfS59MxXicsh1lHYOEsarDTfC4vMj7VibrKMxwHIeEVA2MNZaoHoQe1N+sxcXFWLp0KRYtWoRt27YNud8DDzyAnTt3BjOK6H3W/RHkHI8sfgLrKKIyTpGNU7aj1KxJLonH7UXFZ41ImZQAbZQMNh9KUnosfF4B9acGX/A2GgSt4BmNRmzevBnbt2/H7t27sWPHDlRWVl60z913342SkpJgxQgLrW4jPup6H3M08+je3QXiZVooJWrUOqtZRyFh6Mwn9XD2uDHl29GzxNZQVHEKxCapUHfMNPLOESpoBa+srAyFhYXQarVQq9VYsmTJRYWtuLgY1113HW644YZgxRA9h8+Bf7e+hJkxsxEni2cdR5TGKybieM8R1jFImOmxOFBe1oDMqTokZ9G5xXEcsqbq0VbfhZ4OO+s4TASt4JlMJuh0uv7Her0eRqNxwD4/+clP8J3vfCdYEUTPI3iwo/Ul6OR6ZCuje9zdcMYrc3DKfgweIbq7VBP/CYKAL0sqwQEoWEi3CfpkTkkGANQej86rvKD10hxsld1AN9clJYmzTV6nG3n9OofXgb9UvQg5L8WilMWi6aii1Ypvdhct1NDZdTDKz2FGwqwh9/Pncw93xcXFeOGFF+B2u3HnnXfi9ttvH3S/Bx54AHPmzMEtt9wS4oTiUHmoCc1nzZi+aCLU8UrWcUQjJl4J3bh41B43Ycq3s6LuFkrQCp7BYMDhw4f7H5tMJuj1gR0D095uhc8nruXrdbpYtLZ2D7tPu7sV/277PyTKkjFb8210dQ7e3TrUtFo1LBYb6xiDypJmY3/TfqR7Bp9f1J/PXWwkEm5UX9r67ovv3LkTPM9j1apVmDNnDrKzswfs85vf/AYHDx7EnDlzghFb9NrqOnF0bzVSJycip5AmcLhQ1jQ9jrxdAXNjd9RNsxa0y4q5c+fi4MGDMJvNsNvtKC0txbx584L1dmFBEAR80f0Z/mp8FpMUuZijmSeaKzuxG6eYhDrnOXR7u1hHYYbui4+svaELH20/gRitErNX5EbdFYw/MvN1kMolqP6imXWUkAvab1uDwYB169Zh9erVuOmmm7B8+XIUFBRgzZo1OH48+mbB7/Fa8a+2/8Mn3fuwSFuEXPVldDKOglwix3jlJHxh/ZR1FGbovvjwms604z+vHIciRo5rVheAV8lZRxIluVKGrGl61J9ojboxeUGdaaWoqAhFRUUDntu6detF+z3xxBPBjMFcjaMKr7f9E+OVk3BDwi2QckPPCkGGlqPMx4GuUlwdd11UfobRdF+8q90GTezAe28XPu4jl0txpqwBX75fDV1mPG5Yczk0CYNPED3YcYcil0n92nek/c7f5u8xg7WvSs0jLkmNKxZm49wXLWir7sT0a4bu1BNp98VparEgO9L9Kd7vfBtzYxcgQzGOdZywliTXQS2JQbn9BKaqp7OOE3LRdF/c5/AOmEpsqKnFrGY7jrxXAWuHAxNmpWDmkkmwezywD3E/98LjDifR49++w+13YW5/jxmsfe02F5w+L6CUIDE9Fkf3n0NqftKgX5wi8b443UAKoo8792F/13tYor2Jil2A5Kmm4ZOufYNe7UQ6ui/+Da/Hh7oTJpw52ACfT8C8/5qGK5ZPhlQefVf+lypnTjqs7XY0nWlnHSVkqOAFyaGuj3HI+jGWaFcgXqZlHSdiZCkmoMfbjVpnFesoIUf3xXt1t9tx6j+1aK3thH68Fot+OBOGiQmsY4WdjHwdYrRKlH9SHzVfIKlJMwjO2k9hf1cprk+4CTFScdwTiRQSToKp6hn4sPM9/DAKV1CI5vvigiDAVGNBw+k2KFRy5F6VAU2iCjKeruouhUTCIXduBr54t7L/y0Okoyu8ADM5THizfTvmxy9GrDS6xriEyiRlLjo87bQaehQRBAGN5W1oONUGrT4GU67OjLqVy4Nh/HQDlBoeJ/fXRMVVHhW8APIIHvyt+s8oUM+CXp7COk7EknBSzIyZjZKOXbSKQhQQBAFVXzbDWG2Bblw8Jl6eSvfqAkQqlyJ/fhba6rrQdDby7+VRwQugA52lkEt45KqmsY4S8cYpJoEDh8PWg6yjkCAzVneg8Ww79BO0yJyqo/GrATZhZipik1U4/v45+LyR/QWSCl6AtLgacdhahusMi+iEDAGO4zAndh4+7NyDTk8H6zgkSDparGgsb4cuKx4ZU5Lp3AoCiYRDwcKJ6G6340xZA+s4QUUFLwB8gg+723dgZsxsxMiok0qoJMgSkaeahjfat1HTZgRy2tyoPWqEOl6BvDkZVOyCKG1yEjLyk3HqP7XoNPWwjhM0VPAC4Ij1U/jgoyV+GLhMPRMewY23Gt9kHYUEkM/rw7kvWyAAmDgzBRIp/aoKtpk3ZEOmkOHQrjPwuL2s4wQF/SsaI5u3Bx927sFszdX0DZQBCSfBt+MW4nPzZzjcTffzIsXpsnr0WBwYN00PRQzPOk5UUMbwuPLGybC0WHGk+GxE9tqkcXhj9GFnCbIUE5EoT2YdJWopJSrcmH4L3qjbAQk4zIotZB2JjEFHczfKP21AUkYsEtNGnstRwnHwOSLziiTQRvqsUrK0mPrtLJz8qA4fv34SOXPS/foiL+WlEMLg8okK3hi0uo04bvsCKxJXsY4S9RL4BCzS3ogPLG+j02vB/HjxLKpL/OfzCThcXAGlWo6MfN3ILwDg8fhQV97q175Zef4dM1L581kpYnkkZ8bh+H9q0drYhbTcwefaPN/4qXpIlOIfKkK/EcbgvY7duEw9A0oJDYAVg3iZFjck3ILT9uP4V+vfYPNG7s33SFXxaQMsLVZMXzgRMhprxwTHcciapsfE6SloqerAuS9bIma4AhW8S1TtqIDJ3Yw8VQHrKOQ8Kqkai7U3gucU2NL8FCrt5awjET9ZO+w4ub8WaZOTkD45iXWcqMZxHC5fko30vCR0NFtx+qM69HT4t3KDmFHBuwQ+wYeSjl2YGTMnKtdlEzspJ8UVsXMxN+4a7Gr/N95qfxVOn5N1LDIMQRDwxTsV4CQcZi7Npg5gIsBxHFImJSJnTjq8XgHlZfVoON0W1ld7VPAuwbGeIwB6Z/sg4pXKZ6Ao8Tvo9nbiheanUO+sYR2JDKHuuAnGagumXTcB6jgF6zjkPHHJakydl4XkzDgYqztw6j916GqzsY51SajgjZLL58QHne/gcs1V9C00DPASBebGLcCMmCuxvfWv+KQzOtfSEzOnzY2v3qtCUkYcJl2RyjoOGYRULsW4AgMmz0kHAFR81oiao0Z4XOHVO5YK3ih93LUPOnkKTQ4dZsYpJ2Fpwkp8Zfscr7X9Ay6fi3Uk8rWje6vhdnpx+fIc+hIpcrHJauTPy0LKpAS0N3bh5IFadDSHz6roVPBGweIx45D1Y8yMmcM6CrkEGmkslmhXwCk48JLxj7B6w+dEjVTG6g7UHjUib24G4vUxrOMQP0ikEqTnJWPK1VngVTJUf9GCL96rhNcj/nt7VPBGYU/HLuSppkEjHXkwLBEnKSfDt2KvhV6egr+2PIsOj5l1pKjldXtx5J0KaBJVmDJvHOs4ZJTUcQrkzc1EyqQEnDtqxL7/+xI9HXbWsYZFBc9PZ+2n0OxqwGXqGayjkDHiOA4zNLORq8rH/xmfR5vbxDpSVDr2wTn0dDhw+bJsSGX0qygccRIO6XnJmLtyCnosTnzwf1+hvaGLdawh0b8yPzh9Trxtfh1zYr8NKUeT00SKPHUBCtSX4yXjH2FytbCOE1WaK8yoPNSEnDnp0E9IYB2HjFHqpERc96MZkPFS7P/HMTSc8m/mm1CjgueH0o7dMMhTkcZnso5CAixblYfLNYX4u2kLWlxNrONEBYfVhc/fOoN4fQymXTeBdRwSILHJalz3oxlISNHg4OunUf5Jveh6RFPBG8FZ+ymcsZ/CFZq5rKOQIJmgnIzLNVfhH6YX0OyK7AUwWfP5BBzaVQ63w4M5t+RRU2aEUcTwmL+6AJlTdTj+wTkcebtCVAPV6V/bMCyeDuxq/xeujrsWvIQGw0ayCcoczNZcjX+a/kID1IPoaGkVjNUWzFqaTb0yI5RUJsGcW/Iw5dtZOPdlCz7afgIuh4d1LABU8Ibk8jnxr9a/IV9VAAOfxjoOCYFxykm4KvYabG/9K83BGQRnyupReagJkwvTMWEmDTCPZBzH4bIF43Hlislore3Ehy99JYoenFTwBuEVvNjR9jLipPHIp16ZUSVDMQ7XxC3BG+2v4AgtKBswZw824Nj755CRr0PBwoms45AQGT89BfNunwZ7twt7t36JhtNtTPNQwbuAW3BjR9tLcAsuFMbOo5kfopCeT8US7Qr8p+t9vG1+HR5BHM0x4cjn9eGLPZU4urcaGVOSMeeWPHASOqeiiX6CFgt/MhOaRCUOvnYKR96pgJtREycVvPN0eTrxsvFP8Pg8mBe3GBJaCSFqxcsSsDThFrS5jXixZTNaXI2sI4WdTlMPPnzpKKo+b8LkqzJQuHIKJFTsopImUYVrfzgDk6/KQPWRZuz50+c492VLyHtxBrXgFRcXY+nSpVi0aBG2bdt20fbTp09j5cqVWLJkCR566CF4PGyqviAIONpzGH9ueRp6eSqujruOlv0h4CUKzItbjBxlHv5uegHvmN9gPh1ZOJxTVrMdR94+i70vfgGrxYHClXmYvmgiXdlFOYlUgumLJuK6n8yEJkGFw8Vn8d4Lh1F9pBkuuzskGYI2itpoNGLz5s3YuXMneJ7HqlWrMGfOHGRnZ/fvs379evz2t7/FjBkzsGHDBrz66qu47bbbghXpIm6fC6ftx/FJ1z74BB8WxF+PZLkhZO9PxI/jOGSrpiBDMR7Heo7g+abHMVU9HTM1c5DOZ0HCha6RRKznlMftRUeTFW11nWiuNKO9vgsSKYeJs1Iwdf44KGL4oL4/CS+JabFY8MPpaDjVivKyBhx5pwJf7KmEYYIWhkkJSEyLhTZFAxkf+IuOoBW8srIyFBYWQqvVAgCWLFmCkpIS3HvvvQCAxsZGOBwOzJgxAwBwyy234LnnnhvVyTma5hGf4EOPrxsdng60uY2od9ai0VWDRFkyroibi1R5RkDu18k4GXhpeJ7glH1ovJTH1dprcYXvKtQ4K/FB1zvwCC5kKiYglc9AkkyPeJkWGkms30VwtM17LM8pt8ODc1+2wOXwwO30wOvyweXwwN7lhNP2zbfz2CQ1Ll+eg4wpyVCOpdBJJeBV3/x6kiuk4D2D/7qSygbuO+xhg7DvcPtdmFvMWS8kkXKj2Fcyyn/PHMZNMyDrMj06TT1oqTCjuaoD1YebUY1mAIBcJYNKw0MZw0PGSyBTyCCTSzCuwADVEGsmjpQhaAXPZDJBp9P1P9br9Th27NiQ23U6HYxG46jeIyFhdON4dIjDeKQDuGxUrxs1TXAPH1SUfUSFuCI0b3QB1udUSrp2VMcaK11anN/76jPime7L+v1Hs+9ojpmYGvyJ8pOTYzEpPzTLrQWtPWawm5HnX0GNtJ0QMhCdU4SMTdAKnsFgQFvbN2MuTCYT9Hr9kNtbW1sHbCeEDETnFCFjE7SCN3fuXBw8eBBmsxl2ux2lpaWYN29e//b09HQoFAocOXIEALBr164B2wkhA9E5RcjYcEIQB0IUFxfjL3/5C9xuN2699VasWbMGa9aswdq1azFt2jSUl5fj4YcfRk9PD/Lz8/H444+D58Oz0wQhoUDnFCGXLqgFjxBCCBELmmmFEEJIVKCCRwghJCpQwSOEEBIVqOARQgiJClTwguDIkSNYuXIlVqxYgTvuuAONjeKfaX+kSYnF6o9//COWLVuGZcuW4cknn2QdhwQJnVOhE9HnlEACbsGCBcLp06cFQRCE1157Tbj77rsZJxpeS0uLsGDBAqGjo0Po6ekRioqKhIqKCtaxRvTJJ58I3/ve9wSn0ym4XC5h9erVQmlpKetYJAjonAqNSD+n6AovwFwuF37xi18gLy8PAJCbm4vm5mbGqYZ3/qTEarW6f1JisdPpdHjwwQfB8zzkcjkmTZqEpqYm1rFIgNE5FTqRfk5RwQswnuexYsUKAIDP58Mf//hHLFy4kHGq4Q02KfFoJx1mIScnp39lgJqaGrz77ruYP38+21Ak4OicCp1IP6eCtlpCNNizZw8ef/zxAc9NnDgRL7/8MlwuFx588EF4PB7cddddjBL6RwjzSYcrKipw11134YEHHsD48eNZxyFjQOeUOETqOUUFbwxuuOEG3HDDDRc939PTg5/97GfQarV44YUXIJfLGaTzn8FgwOHDh/sfXzgpsZgdOXIEa9euxYYNG7Bs2TLWccgY0TnFXiSfU9SkGQTr16/HuHHj8Oyzz4bFPIYjTUosVs3Nzfj5z3+Op59+OuJOTDIQnVOhEennFM2lGWCnTp3CzTffjOzsbMhkvRfQer0eW7duZZxseINNSix2v/3tb/HGG28gKyur/7lVq1bh+9//PsNUJNDonAqdSD+nqOARQgiJCtSkSQghJCpQwSOEEBIVqOARQgiJClTwCCGERAUqeIQQQqICFTxCCCFRgQoeIYSQqEAFjxBCSFT4/wFpRIoKIB8e7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 504x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "sns.set(palette=\"muted\", color_codes=True)\n",
    "rs = np.random.RandomState(10)\n",
    "d = rs.normal(size=100)\n",
    "f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True)\n",
    "sns.distplot(d, kde=False, color=\"b\", ax=axes[0, 0])\n",
    "sns.distplot(d, hist=False, rug=True, color=\"r\", ax=axes[0, 1])\n",
    "sns.distplot(d, hist=False, color=\"g\", kde_kws={\n",
    "                \"shade\": True}, ax=axes[1, 0])\n",
    "sns.distplot(d, color=\"m\", ax=axes[1, 1])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n",
      "   SepalLength  SepalWidth  PetalLength  PetalWidth\n",
      "0          5.1         3.5          1.4         0.2\n",
      "1          4.9         3.0          1.4         0.2\n",
      "2          4.7         3.2          1.3         0.2\n",
      "3          4.6         3.1          1.5         0.2\n",
      "4          5.0         3.6          1.4         0.2\n",
      "(150, 4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/hn/Library/Python/3.7/lib/python/site-packages/seaborn/distributions.py:2103: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "ds_iris = datasets.load_iris()\n",
    "print(ds_iris.target_names)\n",
    "df_iris = pd.DataFrame(ds_iris.data, columns=[\n",
    "                        'SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'])\n",
    "print(df_iris.head())\n",
    "print(df_iris.shape)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2)\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True)  # set设置主题，调色板更常用\n",
    "sns.distplot(df_iris['PetalLength'], ax=axes[0],\n",
    "                kde=True, rug=True)        # kde 密度曲线  rug 边际毛毯\n",
    "sns.kdeplot(df_iris['PetalLength'], ax=axes[1],\n",
    "            shade=True)                     # shade  阴影\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n",
      "   SepalLength  SepalWidth  PetalLength  PetalWidth\n",
      "0          5.1         3.5          1.4         0.2\n",
      "1          4.9         3.0          1.4         0.2\n",
      "2          4.7         3.2          1.3         0.2\n",
      "3          4.6         3.1          1.5         0.2\n",
      "4          5.0         3.6          1.4         0.2\n",
      "(150, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "ds_iris = datasets.load_iris()\n",
    "print(ds_iris.target_names)\n",
    "df_iris = pd.DataFrame(ds_iris.data, columns=[\n",
    "                        'SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'])\n",
    "print(df_iris.head())\n",
    "print(df_iris.shape)\n",
    "\n",
    "target = pd.DataFrame(ds_iris.target, columns=['target'])\n",
    "sns.boxplot(x=target['target'], y=df_iris['SepalWidth'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3.7.3 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
